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Título: Modeling of soil organic carbon loss by water erosion on a tropical watershed
Título(s) alternativo(s): Modelagem da perda de carbono orgânico do solo por erosão hídrica em uma bacia hidrográfica tropical
Palavras-chave: Soil conservation
Carbon cycle
Erosion Potential Method (EPM)
Conservação do solo
Ciclo do carbono
Método de Erosão Potencial
Data do documento: 2021
Editor: Universidade Federal do Ceará (UFC)
Citação: LENSE, G. H. E. et al. Modeling of soil organic carbon loss by water erosion on a tropical watershed. Revista Ciência Agronômica, Fortaleza, v. 52, n. 1, p.1-8, 2021. DOI: 10.5935/1806-6690.20210011.
Resumo: Water erosion under tropical climate conditions is one of the main processes that change the balance between the inputs and outputs of soil organic carbon (SOC). Water erosion modeling using the Erosion Potential Method (EPM) can be used as an alternative to assist in understand soil carbon dynamics and its interaction with the erosive process. In this context, the objective of the study was to estimate carbon losses by water erosion in a watershed with a wide land-use diversity. The modelling was performed based on the soil organic matter content (SOM) of the area, and the estimated soil losses, according EPM. To the SOM determination, soil samples were collected 50 points (0-0.2 m) distributed in the watershed. The data analysis was performed using remote sensing techniques and a Geographic Information System, which was also used to interpolate the SOM content, through the use of the ordinary kriging. The results showed that from 126.53 Mg year-1 of the total eroded organic carbon estimated, 111.60 Mg year-1 were deposited in relief depressions, while 14.93 Mg year-1 reached the water body system. The applied methodology represents a cost-effective and relatively fast method to estimate the soil organic carbon loss by water erosion and allows the determination of the areas that most need intervention, aiming to decrease the impact of agriculture on greenhouse gas emissions. The main advantage of this method is the little input data requirement, which increases the possibility of application in poorly studied regions.
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